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How Bioinformatics is Transforming Biology

Author: Prachi Fozdar

Editor: Simona Hausleitner

Bioinformatics is the intersection of various STEM fields, most importantly computer science and biology. It involves the storage and analysis of biological data, and it is usually used for projects that generate large data sets of DNA and Amino Acid sequences. Developing software tools to produce useful biological information is an important part of bioinformatics. Bioinformatics has majorly transformed biology through genomics, proteomics, structural biology, and machine learning.

Genomics is a discipline in genetics that applies recombinant DNA, DNA sequencing methods, and bioinformatics to sequence, assemble, and analyze the function and structure of genomes (DNA sequences that code heritable genetic material). The field includes efforts to determine the entire DNA sequence of organisms and fine-scale genetic mapping. Bioinformatics tools aid in sequencing and annotating genomes and their observed mutations. It also plays a role in the textual mining of biological literature for organizing and analyzing biological data. It is also important in the development of biological and gene ontology. Most often, bioinformatics plays a role in the analysis of expression, regulation, and comparison of genes and proteins in the understanding of evolutionary aspects of molecular biology.

Proteomics refers to the study of the full protein, specifically their structure and function, or proteome collection. As they are the key components of cells’ physiological metabolic pathways, proteins are essential components of living organisms. Founded on the basis of research and development of the Human Genome Project, proteomics involves studying the proteome from the overall structure of the intracellular protein and its unique activity patterns. Some of the other most important applications of bioinformatics through proteomics include confirming evolutionary relationships and predicting the shapes of proteins.

The next generation of bioinformatics, referred to as systems biology, is currently being studied by researchers and scientists. Systems biology is an approach to solving new and complicated biological problems. In order to establish a system view of a biological organism, the incorporation of genomics, proteomics, and bioinformatics knowledge is involved. For instance, through systems biology, we can find out how a signaling pathway operates in the cell. Using systems biology, the genes that are involved in this pathway, how they interact, and how modifications affect the results can all be modelled. From something as small as a single cell to something as large as a whole ecosystem, bioinformatics can be used to solve biological puzzles. Overall, bioinformatics helps analyze and catalogue the biological pathways and networks that are an important part of systems biology.

Machine learning is presently used in genomic sequencing, protein structure determination, microarray analysis, evolutionary phylogenetic tree building, and metabolic pathway determination. Gene prediction is carried out in a variety of ways by machine learning algorithms, including inputting vast amounts of DNA sequences and comparing them to known gene libraries and their noted positions. Unrecognized genes in the sequence are detected, among other factors, by machine learning programs that predict their function based on the gene locus. Finally, to determine evolutionary trees, the analysis of the genomes of several distinct organisms is used. Machine learning algorithms predict the protein structure by studying the amino acid sequence. The number of possible protein structures with similar amino acid sequences is immense, and so the several thousands possible conformations are better analyzed using methods of estimation.

Bioinformatics is evolving from researching single genes in isolation to discovering cellular networks of genes, understanding their complex interactions, and recognizing their function in disease. Many are hopeful that it will result in a new era of medicine, allowing researchers to better target drug discovery and individualized therapy.


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